amd-torchvision-device-gfx950

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torchvision-device-gfx950

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low technical risks but raises concerns due to its incomplete metadata and placeholder content, suggesting it may not have been fully developed or maintained.

  • Low effort shown in package development
  • Missing maintainer history and author details
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on device-specific optimizations.
  • Shell: No shell executions detected, aligning with expectations for a package that appears to be related to graphics processing unit optimization.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low effort and could potentially be suspicious due to lack of maintainer history and missing author details.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amd-torchvision-device-gfx950 
Create a small machine learning application using the 'amd-torchvision-device-gfx950' package. This package is designed to optimize PyTorch models for AMD GPUs with the GFX950 architecture, which is commonly found in Radeon RX Vega series cards. Your task is to develop a utility that can classify images into predefined categories using a pre-trained model optimized for AMD GPUs.

The application should have the following features:
1. Load a pre-trained image classification model compatible with 'amd-torchvision-device-gfx950'.
2. Provide a user-friendly interface where users can upload images for classification.
3. Display the top 5 predicted categories along with their confidence scores.
4. Allow users to save the results of the classification as a text file.

Steps to create the application:
1. Set up your development environment with Python and install the necessary packages including 'amd-torchvision-device-gfx950'.
2. Import the pre-trained model provided by 'amd-torchvision-device-gfx950'. Ensure it is correctly configured to run on AMD GPUs.
3. Develop a function that preprocesses uploaded images to match the input requirements of the model.
4. Implement the image classification functionality using the imported model and display the results.
5. Design a simple GUI using a library like Tkinter or PyQt for users to interact with the application.
6. Add functionality to save the classification results to a text file.
7. Test the application thoroughly to ensure it works as expected on AMD GPUs.
8. Document your code and provide instructions on how to set up and run the application.

💬 Discussion Feed

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